Scraper¶
In [1]:
import os
from google_images_download import google_images_download
In [2]:
root_dir ='data_food_classifier_MG'
In [3]:
response = google_images_download.googleimagesdownload() #class instantiation
arguments_1 = {"keywords":"fast food, hamburger, kebab, pizza, french fries, chicken popeyes, burito, unhealthy food, unhealthy diet, fatty food, hot dog",
"limit":100,
"silent_mode":True,
"format":"jpg",
"prefix":"fast_food",
"output_directory":os.getcwd(),
"image_directory":'data_food_classifier_MG'} #creating list of arguments
response.download(arguments_1) #passing the arguments to the function
arguments_2 = {"keywords":"healthy meal, vegetables, fruit, fit meal, walnuts dinner, fish dinner, green beans dinner, healthy diet, salad, diet food",
"limit":100,
"silent_mode":True,
"format":"jpg",
"prefix":"slow_food",
"output_directory":os.getcwd(),
"image_directory":'data_food_classifier_MG'} #creating list of arguments
response.download(arguments_2) #passing the arguments to the function
Downloading images for: fast food ... Unfortunately all 100 could not be downloaded because some images were not downloadable. 79 is all we got for this search filter! Downloading images for: hamburger ... Unfortunately all 100 could not be downloaded because some images were not downloadable. 0 is all we got for this search filter! Downloading images for: kebab ... Unfortunately all 100 could not be downloaded because some images were not downloadable. 86 is all we got for this search filter! Downloading images for: pizza ... Unfortunately all 100 could not be downloaded because some images were not downloadable. 84 is all we got for this search filter! Downloading images for: french fries ... Unfortunately all 100 could not be downloaded because some images were not downloadable. 88 is all we got for this search filter! Downloading images for: chicken popeyes ... Unfortunately all 100 could not be downloaded because some images were not downloadable. 77 is all we got for this search filter! Downloading images for: burito ... Unfortunately all 100 could not be downloaded because some images were not downloadable. 79 is all we got for this search filter! Downloading images for: unhealthy food ... Unfortunately all 100 could not be downloaded because some images were not downloadable. 89 is all we got for this search filter! Downloading images for: unhealthy diet ... Unfortunately all 100 could not be downloaded because some images were not downloadable. 92 is all we got for this search filter! Downloading images for: fatty food ... Unfortunately all 100 could not be downloaded because some images were not downloadable. 0 is all we got for this search filter! Downloading images for: hot dog ... Unfortunately all 100 could not be downloaded because some images were not downloadable. 86 is all we got for this search filter! Downloading images for: healthy meal ... Unfortunately all 100 could not be downloaded because some images were not downloadable. 88 is all we got for this search filter! Downloading images for: vegetables ... Unfortunately all 100 could not be downloaded because some images were not downloadable. 0 is all we got for this search filter! Downloading images for: fruit ... Unfortunately all 100 could not be downloaded because some images were not downloadable. 90 is all we got for this search filter! Downloading images for: fit meal ... Unfortunately all 100 could not be downloaded because some images were not downloadable. 87 is all we got for this search filter! Downloading images for: walnuts dinner ... Downloading images for: fish dinner ... Unfortunately all 100 could not be downloaded because some images were not downloadable. 91 is all we got for this search filter! Downloading images for: green beans dinner ... Unfortunately all 100 could not be downloaded because some images were not downloadable. 0 is all we got for this search filter! Downloading images for: healthy diet ... Unfortunately all 100 could not be downloaded because some images were not downloadable. 92 is all we got for this search filter! Downloading images for: salad ... Unfortunately all 100 could not be downloaded because some images were not downloadable. 96 is all we got for this search filter! Downloading images for: diet food ... Unfortunately all 100 could not be downloaded because some images were not downloadable. 0 is all we got for this search filter!
Out[3]:
({'healthy meal': ['C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 1.healthy-dinner-collection-main-image.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 2.IMG_16162-1.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 3.Healthy-Meal-Prep-.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 4.teriyaki-chicken-meal-prep-4.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 5.best-healthy-dinner-recipes-1567029863.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 6.healthy-chicken-fajitas-meal-prep-square.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 7.dfe06fb20721a3a25f7b515638c7db75.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 8.Blackened-Shrimp-Meal-Prep9.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 9.Meal-Prep-Chickpea-Salad1.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 10.chicken-satay-salad.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 11.Crispy-Cauliflower-Tacos-036.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 12.Healthy-Meal-Prep-Baked-Turkey-Meatballs--500x500.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 13.HEALTHY-DINNER-IDEAS.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 14.maxresdefault.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 15.Korean-Chicken-Meal-Prep-Bowls-6.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 16.tarragon-pesto-e1526480069676-920x703.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 17.sun-basket.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 18.Whole_30_Chicken_Salad_010-sq.jpg',
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'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 21.healthy-meal-prep-recipes.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 22.healthy-meals-for-easy-meal-prep.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 23.lunch-round-up-new.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 24.28-Day-Healthy-Meal-Plan-800x1000.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 25.light-dinners-coconut-shrimp-rice-1566498330.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 26.delish-pan-fried-tilapia-377-1543266609.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 27.GettyImages-588978788.jpg',
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'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 29.OLI-1018_Healthy-PiriPiriChickenMozzarellaStacks_28557-5cf6a85.jpg',
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'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 34.Healthier-One-Pot-Sesame-Chicken-Healthy-One-Pot-Meals-Recipe-750x498.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 35.238055.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 36.RFO-472x310-ChickenTrayBake-acae5193-a2c9-4a58-9c9c-cee82a565474-0-472x310.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 37.Grilled-Chicken-Meal-Prep-Bowls-4-Ways-for-Clean-Eating.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 38.maxresdefault.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 39.20-Minute-Meal-Prep-Chicken-and-Broccoli-9.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 40.chickenparmstuffedpeppers1-1519936991.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 41.healthy-meal-plan-4.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 42.Green-Chef.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 43.Healthy-Meals-Mediterranean-Panzanella-Salad-thumb.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 44.5-easy-and-healthy-meal-prep-lunch-ideas-149262-2.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 45.fresh-summer-meal-plan.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 46.turkey-taco-lunch-bowls_Cheesy-Broccoli-Cheddar-Chicken-and-Rice-Bowls-Casserole-Meal-Prep.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 47.Portion-Plate.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 48.35-Healthy-Meal-Sized-Salads-You-Need-to-Make-Whole30-Keto-Paleo-Gluten-Free-Low-Carb-Recipes.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 49.hellofresh.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 50.1140-nutrients-food-loved-ones-caregiving.imgcache.rev17dd9e9578e4259ab90ca152af4057e9.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 51.105-Meal-planning-for-simple-quick-healthy-cooking_1081277720.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 52.Honey-Garlic-Chicken-Stir-Fry_16x9_Healthy-Meal-Plans-Thumb.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 53.Ground-Turkey-Meal-Prep-Bowls-4.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 54.Signature-Bowl.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 55.snack-boxes-5-copy.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 56.4-Healthy-Meal-Tips-for-Type-2-Diabetes-722x406.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 57.GRT-12865-22_Insanely_Easy_and_Quick_Healthy_Meals_for_One-1296x728-header-1296x728.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 58.mix-match-meal-prep-21.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 59.IMG_2708-copy.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 60.greek-healthy-meal-prep-whole30-paleo-3-700x1050.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 61.Stuffed-avocado.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 62.2017-31-05_Meal_Prep_Hero_Blog_730x485.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 63.Asian-Chicken-Rice-Bowl_EXPS_SDAS17_201063_D04_11_5b.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 64.healthy_meal_prep_time_saving_plans_to_prep_and_portion_your_weekly_meals-9781465464866.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 65.BFV13132_HealthyMeal-PrepChickenSaladPockets-ThumbTextless1080.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 66.6-ingredient-curry-chicken-meal-prep.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 67.best-healthy-dinner-recipes-1567029863.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 68.50-Quick-Healthy-Dinners-30-Minutes-Or-Less.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 69.healthy-meal-plan-index-1535040812.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 70.lunch-box-ideas-meal-prep.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 71.Meal-Prep-Roasted-Veggies-and-Chicken-2.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 72.1140-why-eating-dead-food-bad-for-health.imgcache.reve0bd88bfc1ba0dbcf7d3479ed798310f.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 73.1519936377-chickenparmstuffedpeppers1.jpg',
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'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 76.Turkey-Medallions-with-Tomato-Salad_EXPS_HCK17_109411_D08_26_7b.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 77.basil-pesto-chicken-pasta-meal-prep-bowls-pic.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 78.raw-pad-thai-choosing-chia.jpg',
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' vegetables': [],
' fruit': ['C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 1.assortment-of-colorful-ripe-tropical-fruits-top-royalty-free-image-995518546-1564092355.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 2.Fruit-Salad-SWP.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 3.What-Your-Favorite-Fruit-Says-About-You.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 4.Culinary_fruits_front_view.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 5.eat-your-fruits-and-veggies-to-avoid-strokes.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 6.Low-Carb-Fruits.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 7.fruits_feature.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 8.maxresdefault.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 9.fruit_selection_155265101_web.jpg',
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'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 21.maxresdefault.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 22.Fruit-Pizza-Design-Square.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 23.fruits-kFLF--621x414@LiveMint.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 24.Fresh-fruit-pretty.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 25.strawberries.jpg',
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'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 27.creative-layout-made-fruits-flat-260nw-1017075634.jpg',
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'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 47.Best-Fruit-Salad-3-500x500.jpg',
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'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 57.pumpkin-a-fruit-hero.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 58.20190719-140436-blackberry_79345.jpg',
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'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 60.mango-620x350_620x350_71505731672.jpg',
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'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 62.28_Grapefruit_Can-You-Identify-Everyday-Objects-By-These-Close-Up-Pictures-_398660866_Africa-Studio.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 63.es_strawberries_809.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 64.Worst-Fruits-Weight-Loss.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 65.920x920.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 66.image.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 67.Pomme.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 68.grapes_3275188k.jpg',
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'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 70.6137879756_4466681697_o.0.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 71.Layered-Fresh-Fruit-Salad_EXPS_HCA18_2778_B04_26_3b-696x696.jpg',
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'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 63.5-reasons-to-add-color-infographic-plus-color-english.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 64.fhm03.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 65.10%20healthy%20foods%20to%20include%20in%20your%20diet%202.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 66.Plant-Based-Diets-blog-cover-300x300-2018-02.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 67.0fde15f4c8bca289e4899768d340c015.jpg',
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'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 69.veggies_healthy_food_uns-1280x720.jpg',
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'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 71.Berkeley-Life-Wellness-Hub-Heart-Vegetables-and-Fruit-Standard-Licence-123RF-61927140-Exp_-No.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 72.UACF_EG_Hero_NoBadge_Healthy-Eating-752x472.jpg',
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'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 77.a-person-sitting-on-the-floor-eating-a-healthy-salad.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 78.healthy-avo-food.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 79.p1chnnl9ih1kvh186p17sf1fj9dsg3.jpg',
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'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 85.scott-warman-525481-unsplash-768x512.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 86.choosing-healthy-foods-inline.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 87.kbe02apg_low-calorie-vegetables_625x300_26_April_19.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 88.Salad---Pixabay.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 89.veggie-heart.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 90.womencommunicationdinnertogetherconceptppc49al.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 91.4288.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 92.diet-and-environmental-impacts-39mir6sjgyh0w3gm23ri80.jpg'],
' salad': ['C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 1.Big-Italian-Salad-760x983.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 2.Autumn-Harvest-Salad-Recipe-with-Sweet-Potatoes-Avocado-Cranberries-and-Pecans-1-1.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 3.exps6498_MRR133247D07_30_5b_WEB-2.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 4.Greek-Salad.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 5.classic-italian-salad-10_ba740feb132926901f5efa7bc2de7ad0.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 6.Everyday-Green-Salad-Recipe-3.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 7.halloumi-salad-0619din.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 8.quick-chopped-salad-recipe-photos-tablefortwoblog-3.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 9.Caesar-Salad-Fifteen-Spatulas-3.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 10.salad_verte_with_15684_16x9.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 11.sandra-lee-food-today-main-181018-02_28c1f1d7033c651ae8bd93a89f929201.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 12.4552561.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 13.caesar-salad-10-1200.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 14.931-diabetic-powerhouse-kale-salad_designed-for-one_071118_3547183137.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 15.mexican-chopped-salad3.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 16.Romaine-Avocado-Chicken-Salad-Recipe-3.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 17.mediterranean-chickpea-salad-fb-ig-1.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 18.caprese-salad-recipe.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 19.wickedspatula-easy-tossed-big-italian-salad-recipe-2.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 20.Caesar-Salad-Recipe-3.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 21.Grilled-Chicken-Cobb-SaladIMG_9150.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 22.Everyday-Mexican-Salad-Recipe-2-1.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 23.Couscous-Summer-Salad-Feature-1.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 24.t-mcdonalds-Premium-Southwest-Salad-with-Buttermilk-Crispy-Chicken.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 25.beetroot_halloumi_salad.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 26.Easy-Apple-Salad-Recipe-2-1200.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 27.Fattoush-Salad-Recipe-1.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 28.best-side-salad-3.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 29.daphne-oz-today-main-190730-03_dcaea29aa7f98f11b9e2cf0696d9f9a2.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 30.Mediterranean-Chopped-Salad-Culinary-Hill-5.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 31.Feta-Romaine-Salad_exps37614_SD2847494A02_12_9bC_RMS.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 32.Summer-Broccoli-Salad-1-725x725.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 33.dads-greek-salad-horiz-a-1600.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 34.201011-xl-jordons-romaine-salad.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 35.cucumber-tomato-salad-4.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 36.RFO-1400x919-Coriander--lime-cucumber-salad-with-chicken-aa3855fc-380a-4b44-9f88-b57a36bf2a88-0-1400x919.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 37.1.-Korean-Green-Salad.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 38.beetroot-feta-grain-salad.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 39.salad-done-right-FT-RECIPE0319.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 40.recipe-229.700x525.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 41.best-watermelon-salad-recipe-1.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 42.GoLive-Amiel-Cottage-Cheese-Salad-Lede.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 43.Skinny-Pink-Salmon-Green-Salad-Recipe-1-700x934.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 44.cuban-pasta-salad-12.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 45.healthy-kale-brocoli-salad-lemon-dressing-recipe-.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 46.recipe-185.700x525.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 47.Al-Desko-Steak-Salad-Leftovers-Recipe.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 48.12superfoodssalad-9-720x405.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 49.stone-house-salad-recipe-2255-1.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 50.1862-1210-CL-Chicken-Salad-V2-78524.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 51.simplest-green-salad-LEDE.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 52.Thai-beef-salad-5.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 53.Everyday-Greens-Salad.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 54.types-of-salad-cobb.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 55.olive-garden-salad-with-copycat-dressing-3-of-8.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 56.fruit-and-vegetable-salad-served-in-lettuce-leaf-500x375.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 57.Guacamole-Tossed-Salad_EXPS_MRMZ16_21265_C09_09_3b-1.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 58.t-mcdonalds-Premium-Bacon-Ranch-Salad-with-Grilled-Chicken.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 59.Edamame-Salad-8501811-February-24-2019-2.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 60.avocado_salad_60227_16x9.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 61.Easy-Green-Salad-4-500x500.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 62.Low-FODMAP-Cobb-Salad-4.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 63.Fruit-and-Berry-Salad1.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 64.Crispy-Buffalo-Ranch-Chicken-Salad-with-Goddess-Dressing-1.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 65.Waldorf-Salad-4.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 66.burrata-salad-recipe-5.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 67.Chopped-Salad-008-800x1000.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 68.3_Ingredient_Hummus_Dressing_FromMyBowl-5.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 69.Italian-Summer-Salad_5_600X900.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 70.salad_005-1-555x740.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 71.Classic-Chicken-Salad-2.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 72.Cauliflower_Salad_08-web.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 73.Watermelon-Salad-Poppyseed-Dressing.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 74.horiatiki-greek-salad-today-041618-tease_79c5041ae6a58da5e333029bbe2c4b88.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 75.P4111233-copy.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 76.Antipasto-Salad-recipe-600x776.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 77.mexican-wedge-salad_thecozyapron_1.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 78.Cucumber-Salad-1.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 79.layered-crunchy-noodle-salad-131650-1.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 80.Lebanese-salad-image-720x540.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 81.1200px-Salad_platter.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 82.winter-greens-and-citrus-salad-1811-p90.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 83.GRILLED-CHICKEN-AVOCADO-AND-MANGO-SALAD-3.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 84.nicoise-salad-I-howsweeteats.com-9.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 85.broccoli-salad-recipe-2.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 86.Green-Goddess-Salad-7-of-7-copy.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 87.chicken-caesar-pasta-salad-recipe.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 88.oil-and-vinegar-salad-dressing-recipe-995915-Final-5ba002b84cedfd00259287af.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 89.Vegan-Southwest-Pasta-Salad-Recipe-7.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 90.New-York-kale-and-chicken-Caesar-salad.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 91.broccoli-crunch-salad-I-howsweeteats.com-8.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 92.scottish-salmon-caesar-salad.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 93.IMG_4753.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 94.Caprese_Salad_Image_1_800_480_85_s_c1.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 95.Greek-Salad-with-Grilled-Chicken-foodiecrush.com-005.jpg',
'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 96.Grilled-Romaine-Salad-SQUARE.jpg'],
' diet food': []},
57)
Image preparation¶
In [4]:
import numpy as np
from PIL import Image
from functions.food_classifier_plotting_functions import plot_accuracy_loss, plot_predictions_or_examples, plot_confusion_matrix
from functions.split_images import split_images
from functions.my_image_data_generator import my_image_data_generator
from functions.correct_predictions import correct_predictions
from functions.my_sequential import my_sequential
%matplotlib inline
Using TensorFlow backend.
Some images may have been saved with the wrong extension.
In [5]:
root_listdir = os.listdir(root_dir)
counter = 0
for index, element in enumerate(root_listdir):
filename = root_dir + '/' + element
try:
im = Image.open(filename)
im.verify() #I perform also verify, don't know if he sees other types o defects
im.close() #reload is necessary in my case
except (OSError, ValueError):
os.remove(filename)
root_listdir[index] = 0
counter += 1
print('%d files deleted due to OSError and ValueError.' %counter)
0 files deleted due to OSError and ValueError.
Splitting images into train, test and valid sets.
In [6]:
root_listdir = [i for i in root_listdir if i != 0]
list_of_images_indices = np.random.choice(len(root_listdir), 60)
list_of_images_names = [root_listdir[i] for i in list_of_images_indices]
plot_predictions_or_examples(list_of_images_names, title="Random image from food class")
In [7]:
fast_food_list = [i for i in root_listdir if 'fast_food' in i]
slow_food_list = [i for i in root_listdir if 'slow_food' in i]
train_examples_fast, valid_examples_fast, test_examples_fast = split_images(fast_food_list, root_dir, '/fast_food/')
train_examples_slow, valid_examples_slow, test_examples_slow = split_images(slow_food_list, root_dir, '/slow_food/')
train_examples = train_examples_fast + train_examples_slow
valid_examples = valid_examples_fast + valid_examples_slow
test_examples = test_examples_fast + test_examples_slow
Classification models¶
In [8]:
%load_ext tensorboard
import tensorflow as tf
import keras
import random
import itertools
import datetime
from sklearn.metrics import confusion_matrix
from keras.preprocessing.image import ImageDataGenerator
from keras import applications, backend as K
from keras.models import Model
from keras.layers import (
Dense,
Activation,
Conv2D,
MaxPool2D,
Flatten,
BatchNormalization,
Dropout,
)
from keras.layers.core import Dense, Flatten
from keras.layers.normalization import BatchNormalization
from keras.optimizers import Adam, RMSprop
from keras.metrics import categorical_crossentropy
from keras_lr_finder import LRFinder
from keras.callbacks import TensorBoard
In [9]:
channels = 3
img_height = img_width = 224
batch_size = 32
train_folder = root_dir + "/train"
test_folder = root_dir + "/test"
valid_folder = root_dir + "/valid"
classes = ["slow_food", "fast_food"]
Logistic regression¶
First attempt should be simple so we will check Logistic Regression on our data.
In [10]:
train_generator = my_image_data_generator(
train_folder, class_mode="binary", shuffle=True
)
test_generator = my_image_data_generator(test_folder, class_mode="binary", shuffle=True)
x_train, y_train = next(train_generator)
x_test, y_test = next(test_generator)
Found 1055 images belonging to 2 classes. Found 226 images belonging to 2 classes.
In [11]:
from sklearn.linear_model import LogisticRegression
logistic = LogisticRegression(solver="liblinear")
logistic.fit(x_train.reshape(batch_size, -1), y_train)
Out[11]:
LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, l1_ratio=None, max_iter=100,
multi_class='warn', n_jobs=None, penalty='l2',
random_state=None, solver='liblinear', tol=0.0001, verbose=0,
warm_start=False)
In [12]:
y_pred = logistic.predict(x_test.reshape(len(x_test), -1))
np.count_nonzero(y_pred == y_test) / len(y_test)
Out[12]:
0.65625
Model accuracy is not satisfactory.
Build Fine-tuned VGG16 model¶
Now we will try to use pretrained model VGG16. The model achieves 92.7% top-5 test accuracy in ImageNet, which is a dataset of over 14 million images belonging to 1000 classes.
In [13]:
K.clear_session()
vgg16_model = applications.vgg16.VGG16()
vgg16_model.summary()
Model: "vgg16" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= input_1 (InputLayer) (None, 224, 224, 3) 0 _________________________________________________________________ block1_conv1 (Conv2D) (None, 224, 224, 64) 1792 _________________________________________________________________ block1_conv2 (Conv2D) (None, 224, 224, 64) 36928 _________________________________________________________________ block1_pool (MaxPooling2D) (None, 112, 112, 64) 0 _________________________________________________________________ block2_conv1 (Conv2D) (None, 112, 112, 128) 73856 _________________________________________________________________ block2_conv2 (Conv2D) (None, 112, 112, 128) 147584 _________________________________________________________________ block2_pool (MaxPooling2D) (None, 56, 56, 128) 0 _________________________________________________________________ block3_conv1 (Conv2D) (None, 56, 56, 256) 295168 _________________________________________________________________ block3_conv2 (Conv2D) (None, 56, 56, 256) 590080 _________________________________________________________________ block3_conv3 (Conv2D) (None, 56, 56, 256) 590080 _________________________________________________________________ block3_pool (MaxPooling2D) (None, 28, 28, 256) 0 _________________________________________________________________ block4_conv1 (Conv2D) (None, 28, 28, 512) 1180160 _________________________________________________________________ block4_conv2 (Conv2D) (None, 28, 28, 512) 2359808 _________________________________________________________________ block4_conv3 (Conv2D) (None, 28, 28, 512) 2359808 _________________________________________________________________ block4_pool (MaxPooling2D) (None, 14, 14, 512) 0 _________________________________________________________________ block5_conv1 (Conv2D) (None, 14, 14, 512) 2359808 _________________________________________________________________ block5_conv2 (Conv2D) (None, 14, 14, 512) 2359808 _________________________________________________________________ block5_conv3 (Conv2D) (None, 14, 14, 512) 2359808 _________________________________________________________________ block5_pool (MaxPooling2D) (None, 7, 7, 512) 0 _________________________________________________________________ flatten (Flatten) (None, 25088) 0 _________________________________________________________________ fc1 (Dense) (None, 4096) 102764544 _________________________________________________________________ fc2 (Dense) (None, 4096) 16781312 _________________________________________________________________ predictions (Dense) (None, 1000) 4097000 ================================================================= Total params: 138,357,544 Trainable params: 138,357,544 Non-trainable params: 0 _________________________________________________________________
In [14]:
model = my_sequential()
for layer in vgg16_model.layers[:-3]:
model.add(layer)
We should pop dense layers.
In [15]:
for layer in model.layers:
layer.trainable = False
model.add(Dense(2, activation="sigmoid"))
model.summary()
Model: "my_sequential_1" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= block1_conv1 (Conv2D) (None, 224, 224, 64) 1792 _________________________________________________________________ block1_conv2 (Conv2D) (None, 224, 224, 64) 36928 _________________________________________________________________ block1_pool (MaxPooling2D) (None, 112, 112, 64) 0 _________________________________________________________________ block2_conv1 (Conv2D) (None, 112, 112, 128) 73856 _________________________________________________________________ block2_conv2 (Conv2D) (None, 112, 112, 128) 147584 _________________________________________________________________ block2_pool (MaxPooling2D) (None, 56, 56, 128) 0 _________________________________________________________________ block3_conv1 (Conv2D) (None, 56, 56, 256) 295168 _________________________________________________________________ block3_conv2 (Conv2D) (None, 56, 56, 256) 590080 _________________________________________________________________ block3_conv3 (Conv2D) (None, 56, 56, 256) 590080 _________________________________________________________________ block3_pool (MaxPooling2D) (None, 28, 28, 256) 0 _________________________________________________________________ block4_conv1 (Conv2D) (None, 28, 28, 512) 1180160 _________________________________________________________________ block4_conv2 (Conv2D) (None, 28, 28, 512) 2359808 _________________________________________________________________ block4_conv3 (Conv2D) (None, 28, 28, 512) 2359808 _________________________________________________________________ block4_pool (MaxPooling2D) (None, 14, 14, 512) 0 _________________________________________________________________ block5_conv1 (Conv2D) (None, 14, 14, 512) 2359808 _________________________________________________________________ block5_conv2 (Conv2D) (None, 14, 14, 512) 2359808 _________________________________________________________________ block5_conv3 (Conv2D) (None, 14, 14, 512) 2359808 _________________________________________________________________ block5_pool (MaxPooling2D) (None, 7, 7, 512) 0 _________________________________________________________________ flatten (Flatten) (None, 25088) 0 _________________________________________________________________ dense_1 (Dense) (None, 2) 50178 ================================================================= Total params: 14,764,866 Trainable params: 50,178 Non-trainable params: 14,714,688 _________________________________________________________________
In [16]:
train_batches = my_image_data_generator(train_folder, batch_size=10, classes=classes)
test_batches = my_image_data_generator(test_folder, batch_size=10, classes=classes)
valid_batches = my_image_data_generator(valid_folder, batch_size=4, classes=classes)
Found 1055 images belonging to 2 classes. Found 226 images belonging to 2 classes. Found 226 images belonging to 2 classes.
Train the fine-tuned VGG16 model¶
In [17]:
model.compile(Adam(lr=0.001), loss="categorical_crossentropy", metrics=["accuracy"])
log_dir = os.path.join(
"logs",
"fit",
datetime.datetime.now().strftime("%Y%m%d-%H%M%S"),
)
tensorboard_callback = TensorBoard(log_dir=log_dir, histogram_freq=1)
history = model.fit_generator(
train_batches,
train_examples // 10,
validation_data=valid_batches,
validation_steps=valid_examples // 4 + 1,
epochs=10,
verbose=2,
callbacks=[tensorboard_callback],
)
predictions = model.predict_generator(test_batches, test_examples // 10)
Epoch 1/10 - 217s - loss: 0.7196 - accuracy: 0.4995 - val_loss: 0.6931 - val_accuracy: 0.5044 Epoch 2/10 - 216s - loss: 0.6931 - accuracy: 0.5091 - val_loss: 0.6931 - val_accuracy: 0.5044 Epoch 3/10 - 217s - loss: 0.6931 - accuracy: 0.4900 - val_loss: 0.6931 - val_accuracy: 0.5044 Epoch 4/10 - 216s - loss: 0.6931 - accuracy: 0.5091 - val_loss: 0.6931 - val_accuracy: 0.5044 Epoch 5/10 - 216s - loss: 0.6931 - accuracy: 0.4986 - val_loss: 0.6931 - val_accuracy: 0.5044 Epoch 6/10 - 216s - loss: 0.6931 - accuracy: 0.5206 - val_loss: 0.6931 - val_accuracy: 0.5044 Epoch 7/10 - 217s - loss: 0.6931 - accuracy: 0.4876 - val_loss: 0.6931 - val_accuracy: 0.5044 Epoch 8/10 - 215s - loss: 0.6931 - accuracy: 0.5019 - val_loss: 0.6931 - val_accuracy: 0.5044 Epoch 9/10 - 642s - loss: 0.6931 - accuracy: 0.4900 - val_loss: 0.6931 - val_accuracy: 0.5044 Epoch 10/10 - 219s - loss: 0.6931 - accuracy: 0.5378 - val_loss: 0.6931 - val_accuracy: 0.5044
In [18]:
predictions = model.predict_generator(test_batches, test_examples // 10)
correct_predictions(test_batches, predictions)
Correct predictions: 0.4690265486725664
In [19]:
plot_accuracy_loss(history)
In [20]:
c = list(
zip(
[i.split("\\")[1] for i in test_batches.filenames],
[1 if i[0] >= 0.5 else 0 for i in predictions],
)
)
random.shuffle(c)
filenames, preds = zip(*c)
In [21]:
wrong_predictions = plot_predictions_or_examples(filenames, preds)
In [22]:
wrong_predictions
Out[22]:
['slow_food 61.fish-packet-asparagus-and-rice.jpg', 'slow_food 28.light-dinners-coconut-shrimp-rice-1566498330.jpg', 'slow_food 40.Al-Desko-Steak-Salad-Leftovers-Recipe.jpg', 'slow_food 46.facebook-cover-art.jpg', 'slow_food 10.rejsh0to_protein-rich-salads_625x300_27_September_19.jpg', 'slow_food 83.insta-14-3.jpg', 'slow_food 58.Stuffed-avocado.jpg', 'slow_food 89.green-bean-bundles-6.jpg', 'slow_food 61.20190719-140436-blackberry_79345.jpg', 'slow_food 83.Stocksy_txpa269f3e29MP100_Medium_1257410-58c70b893df78c353cdeb0bd.jpg', 'slow_food 77.a2679-1534700268-800.jpg', 'slow_food 12.Healthy-Meal-Prep-Baked-Turkey-Meatballs--500x500.jpg', 'slow_food 82.easy-kielbasa-sheet-pan-dinner-with-veggies-image.jpg', 'slow_food 63.secret-to-a-healthy-heart.jpg', 'slow_food 13.bacon-green-beans-side-dish-keto-gluten-free-whole30-paleo-4-of-4-1.jpg', 'slow_food 3.1504633899842-recipeMainImagePath.jpg', 'slow_food 31.salmon-518497_1280-1080x675.jpg', 'slow_food 5.GM-Diet--Is-It-The-Best-Plan-For-Weight-Loss-In-7-Days.jpg', 'slow_food 79.5321_4k.jpg', 'slow_food 44.best-side-salad-3.jpg', 'slow_food 54.wide_25590.jpg', 'slow_food 58.032019__dirty_dozen_clean_15_pesti.2e16d0ba.fill-735x490.jpg', 'slow_food 67.Plant-Based-Diets-blog-cover-300x300-2018-02.jpg', 'slow_food 82.maxfitmeals_breakfastburrito2017_web.jpg', 'slow_food 76.ThinkstockPhotos-179103931-Diet-2048x1024.jpg', 'slow_food 45.Header_2_1600x.jpg']
In [23]:
test_labels = np.array([0 if "slow_food" in f else 1 for f in test_batches.filenames])[
: len(predictions)
]
cm = confusion_matrix(test_labels, np.round(predictions[:, 0]))
In [24]:
plot_confusion_matrix(cm, classes, title="Confusion Matrix")
Confusion matrix, without normalization [[ 0 114] [ 0 106]]
Model performed better than simple guessing, but it is still less accurate than Logistic Regression.
Sequential model¶
Pretrained model didn't pass the exam. Now we will try create our own model.
In [25]:
K.clear_session()
model = my_sequential()
model.add(
Conv2D(
8,
kernel_size=(3, 3),
padding="same",
input_shape=(img_width, img_height, channels),
)
)
model.add(Activation("relu"))
model.add(MaxPool2D(pool_size=(2, 2)))
model.add(Conv2D(16, kernel_size=(3, 3), padding="same"))
model.add(BatchNormalization())
model.add(Activation("relu"))
model.add(MaxPool2D(pool_size=(2, 2)))
model.add(Conv2D(32, kernel_size=(3, 3), padding="same"))
model.add(BatchNormalization())
model.add(Activation("relu"))
model.add(MaxPool2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(2, activation="sigmoid"))
model.compile(optimizer="rmsprop", loss="binary_crossentropy", metrics=["accuracy"])
model.summary()
Model: "my_sequential_1" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= conv2d_1 (Conv2D) (None, 224, 224, 8) 224 _________________________________________________________________ activation_1 (Activation) (None, 224, 224, 8) 0 _________________________________________________________________ max_pooling2d_1 (MaxPooling2 (None, 112, 112, 8) 0 _________________________________________________________________ conv2d_2 (Conv2D) (None, 112, 112, 16) 1168 _________________________________________________________________ batch_normalization_1 (Batch (None, 112, 112, 16) 64 _________________________________________________________________ activation_2 (Activation) (None, 112, 112, 16) 0 _________________________________________________________________ max_pooling2d_2 (MaxPooling2 (None, 56, 56, 16) 0 _________________________________________________________________ conv2d_3 (Conv2D) (None, 56, 56, 32) 4640 _________________________________________________________________ batch_normalization_2 (Batch (None, 56, 56, 32) 128 _________________________________________________________________ activation_3 (Activation) (None, 56, 56, 32) 0 _________________________________________________________________ max_pooling2d_3 (MaxPooling2 (None, 28, 28, 32) 0 _________________________________________________________________ flatten_1 (Flatten) (None, 25088) 0 _________________________________________________________________ dense_1 (Dense) (None, 2) 50178 ================================================================= Total params: 56,402 Trainable params: 56,306 Non-trainable params: 96 _________________________________________________________________
In [26]:
train_batches = my_image_data_generator(
train_folder, classes=classes, shuffle=True
)
test_batches = my_image_data_generator(test_folder, classes=classes)
Found 1055 images belonging to 2 classes. Found 226 images belonging to 2 classes.
In [27]:
model.fit_generator(train_batches, train_examples // batch_size, epochs=2)
Epoch 1/2 32/32 [==============================] - 36s 1s/step - loss: 3.1560 - accuracy: 0.6256 Epoch 2/2 32/32 [==============================] - 36s 1s/step - loss: 1.4072 - accuracy: 0.7395
Out[27]:
<keras.callbacks.callbacks.History at 0x1ec7207ba20>
In [28]:
y_pred = model.predict_generator(test_batches, test_examples // batch_size, workers=4)
correct_predictions(test_batches, y_pred)
Correct predictions: 0.504424778761062
In [29]:
c = list(
zip(
[i.split("\\")[1] for i in test_batches.filenames],
[1 if i[0] >= 0.5 else 0 for i in y_pred],
)
)
random.shuffle(c)
filenames_2, preds_2 = zip(*c)
In [30]:
wrong_predictions_2 = plot_predictions_or_examples(filenames_2, preds_2)
In [31]:
wrong_predictions_2
Out[31]:
['fast_food 76.1475-shutterstock_295119902.jpg', 'fast_food 56.cold-fry-frites-patricia-wells-030617.jpg', 'fast_food 47.P6101542.jpg', 'fast_food 27.Big__fatty.jpg', 'fast_food 32.FryCloseupOPT-480x270.jpg', 'fast_food 28.IMG_5038.jpg', 'fast_food 62.006.jpg', 'fast_food 48.white-and-brown-bread-which-may-be-an-unhealthy-food.jpg', 'fast_food 63.An-Unhealthy-Diet-Could-Hurt-Your-Brain-752x472.jpg', 'fast_food 51.175841927-463310.jpg', 'fast_food 70.french-fries-salted-egg-50461022.jpg', 'fast_food 60.air-fries-recipe-for-the-best-healthy-fries_3729.jpg', 'fast_food 81.Kebab-800x450.jpg', 'fast_food 78.School+Junk+Food.jpg', 'fast_food 64.Twister%2Bz%2BKFC%2B14.jpg', 'fast_food 46.kfc.jpg', 'fast_food 79.DvgP42uXgAETT7V.jpg', 'fast_food 59.15339996.jpg', 'fast_food 25.OCR-L-POPEYES-1022-1.jpg', 'fast_food 51.BN-RN270_PORTIO_P_20170106154933.jpg', 'fast_food 19.gf-1y8X-q6rp-7H9N_mcdonalds-przyjedzie-do-ciebie-664x442-nocrop.jpg', 'fast_food 54.burito_s_kuricej_i_fasolyu_2.jpg', 'fast_food 40.topimage-pizza-special17-800x500.jpg', 'fast_food 13.pizza-930x530.jpg', 'fast_food 28.10-Unhealthy-Foods-You-Need-To-Ditch-Right-Now.jpg', 'fast_food 71.Difference-between-healthy-and-unhealthy-foods.jpg', 'fast_food 80.Air-Fryer-Homemade-Fries-1.jpg', 'fast_food 71.popeyes.jpg', 'fast_food 74.Shoestring-French-Fries-4-500x500.jpg', 'fast_food 73.berber-q-chicken-shish-kebab-1803100a-2ca2-4ae0-8082-9decec65d71c_s640x0_c4725x2760_l0x1787_q70_noupscale.jpg', 'fast_food 47.Pizza-unhealthy-food.jpg']
In [32]:
test_labels = np.array([0 if "slow_food" in f else 1 for f in test_batches.filenames])[
: len(y_pred)
]
cm = confusion_matrix(test_labels, np.round(y_pred[:, 0]))
In [33]:
plot_confusion_matrix(cm, classes, title="Confusion Matrix")
Confusion matrix, without normalization [[114 0] [110 0]]
Model is much worse than previous ones. It means that simple model won't be enough, because we don't have big dataset.
Transfer learning¶
Last attempt is transfer learning. We will use VGG16 again but this time in other way.
In [34]:
K.clear_session()
train_batches_1 = my_image_data_generator(train_folder, class_mode=None)
train_batches_2 = my_image_data_generator(train_folder, class_mode=None)
valid_batches = my_image_data_generator(valid_folder, class_mode=None)
test_batches = my_image_data_generator(test_folder, class_mode="binary")
Found 1055 images belonging to 2 classes. Found 1055 images belonging to 2 classes. Found 226 images belonging to 2 classes. Found 226 images belonging to 2 classes.
In [35]:
model = applications.VGG16(include_top=False, weights="imagenet")
model.summary()
Model: "vgg16" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= input_1 (InputLayer) (None, None, None, 3) 0 _________________________________________________________________ block1_conv1 (Conv2D) (None, None, None, 64) 1792 _________________________________________________________________ block1_conv2 (Conv2D) (None, None, None, 64) 36928 _________________________________________________________________ block1_pool (MaxPooling2D) (None, None, None, 64) 0 _________________________________________________________________ block2_conv1 (Conv2D) (None, None, None, 128) 73856 _________________________________________________________________ block2_conv2 (Conv2D) (None, None, None, 128) 147584 _________________________________________________________________ block2_pool (MaxPooling2D) (None, None, None, 128) 0 _________________________________________________________________ block3_conv1 (Conv2D) (None, None, None, 256) 295168 _________________________________________________________________ block3_conv2 (Conv2D) (None, None, None, 256) 590080 _________________________________________________________________ block3_conv3 (Conv2D) (None, None, None, 256) 590080 _________________________________________________________________ block3_pool (MaxPooling2D) (None, None, None, 256) 0 _________________________________________________________________ block4_conv1 (Conv2D) (None, None, None, 512) 1180160 _________________________________________________________________ block4_conv2 (Conv2D) (None, None, None, 512) 2359808 _________________________________________________________________ block4_conv3 (Conv2D) (None, None, None, 512) 2359808 _________________________________________________________________ block4_pool (MaxPooling2D) (None, None, None, 512) 0 _________________________________________________________________ block5_conv1 (Conv2D) (None, None, None, 512) 2359808 _________________________________________________________________ block5_conv2 (Conv2D) (None, None, None, 512) 2359808 _________________________________________________________________ block5_conv3 (Conv2D) (None, None, None, 512) 2359808 _________________________________________________________________ block5_pool (MaxPooling2D) (None, None, None, 512) 0 ================================================================= Total params: 14,714,688 Trainable params: 14,714,688 Non-trainable params: 0 _________________________________________________________________
Training VGG16 on our data should give us the bottleneks of our images. In this case we will use VGG16 predictions to train our sequential model. It means that we changed sets of images into sets of bottlenecks which could be more helpful in detecting category.
In [36]:
bottleneck_features_train = model.predict_generator(
train_batches_1, train_examples // batch_size, verbose=1, workers=4
)
bottleneck_features_valid = model.predict_generator(
valid_batches, test_examples // batch_size, verbose=1, workers=4
)
bottleneck_features_test = model.predict_generator(
test_batches, test_examples // batch_size, verbose=1, workers=4
)
bottleneck_features_train.shape
32/32 [==============================] - 177s 6s/step 7/7 [==============================] - 38s 5s/step 7/7 [==============================] - 40s 6s/step
Out[36]:
(1024, 7, 7, 512)
In [37]:
model = my_sequential()
model.add(Flatten(input_shape=bottleneck_features_train.shape[1:]))
model.add(Dense(256, activation="relu"))
model.add(Dropout(0.5))
model.add(Dense(1, activation="sigmoid"))
for layer in model.layers[:-1]:
layer.trainable = False
model.compile(
optimizer=RMSprop(learning_rate=0.01, rho=0.9),
loss="binary_crossentropy",
metrics=["accuracy"],
)
log_dir = os.path.join(
"logs",
"fit",
datetime.datetime.now().strftime("%Y%m%d-%H%M%S"),
)
tensorboard_callback = TensorBoard(log_dir=log_dir, histogram_freq=1)
model.summary()
Model: "my_sequential_1" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= flatten_1 (Flatten) (None, 25088) 0 _________________________________________________________________ dense_1 (Dense) (None, 256) 6422784 _________________________________________________________________ dropout_1 (Dropout) (None, 256) 0 _________________________________________________________________ dense_2 (Dense) (None, 1) 257 ================================================================= Total params: 6,423,041 Trainable params: 257 Non-trainable params: 6,422,784 _________________________________________________________________
In [38]:
labels = np.array([0 if "slow_food" in f else 1 for f in train_batches_2.filenames])[
: len(bottleneck_features_train)
]
val_labels = np.array([0 if "slow_food" in f else 1 for f in valid_batches.filenames])[
: len(bottleneck_features_valid)
]
In [39]:
lr_finder = LRFinder(model)
lr_finder.find(bottleneck_features_train, labels, 0.0000001, 1, 200, 20)
lr_finder.plot_loss()
Epoch 1/20 1024/1024 [==============================] - 0s 255us/step - loss: 0.8079 - accuracy: 0.5078 Epoch 2/20 1024/1024 [==============================] - 0s 168us/step - loss: 0.8230 - accuracy: 0.4922 Epoch 3/20 1024/1024 [==============================] - 0s 191us/step - loss: 0.7936 - accuracy: 0.4893 Epoch 4/20 1024/1024 [==============================] - 0s 178us/step - loss: 0.8005 - accuracy: 0.5156 Epoch 5/20 1024/1024 [==============================] - 0s 175us/step - loss: 0.7965 - accuracy: 0.5205 Epoch 6/20 1024/1024 [==============================] - 0s 176us/step - loss: 0.8018 - accuracy: 0.4961 Epoch 7/20 1024/1024 [==============================] - 0s 174us/step - loss: 0.8204 - accuracy: 0.5049 Epoch 8/20 1024/1024 [==============================] - 0s 182us/step - loss: 0.8069 - accuracy: 0.4805 Epoch 9/20 1024/1024 [==============================] - 0s 168us/step - loss: 0.7981 - accuracy: 0.48340s - loss: 0.7901 - accuracy: 0. Epoch 10/20 1024/1024 [==============================] - 0s 150us/step - loss: 0.8020 - accuracy: 0.4863 Epoch 11/20 1024/1024 [==============================] - 0s 149us/step - loss: 0.8139 - accuracy: 0.4775 Epoch 12/20 1024/1024 [==============================] - 0s 149us/step - loss: 0.7888 - accuracy: 0.4854 Epoch 13/20 1024/1024 [==============================] - 0s 142us/step - loss: 0.7959 - accuracy: 0.5176 Epoch 14/20 1024/1024 [==============================] - 0s 156us/step - loss: 1.2102 - accuracy: 0.4863 Epoch 15/20 600/1024 [================>.............] - ETA: 0s - loss: 1.8664 - accuracy: 0.4867
In [40]:
lr_finder.plot_loss_change(
sma=20, n_skip_beginning=15, n_skip_end=2, y_lim=(-0.05, 0.06)
)
In [41]:
model.fit(
bottleneck_features_train,
labels,
validation_data=(bottleneck_features_valid[: len(val_labels)], val_labels),
epochs=20,
batch_size=batch_size,
)
for layer in model.layers[:-1]:
layer.trainable = True
model.compile(
optimizer=RMSprop(learning_rate=0.001, rho=0.9),
loss="binary_crossentropy",
metrics=["accuracy"],
)
history_2 = model.fit(
bottleneck_features_train,
labels,
validation_data=(bottleneck_features_valid[: len(val_labels)], val_labels),
epochs=20,
batch_size=batch_size,
callbacks=[tensorboard_callback],
)
Train on 1024 samples, validate on 224 samples Epoch 1/20 1024/1024 [==============================] - 0s 341us/step - loss: 0.7660 - accuracy: 0.5107 - val_loss: 0.6624 - val_accuracy: 0.6161 Epoch 2/20 1024/1024 [==============================] - 0s 320us/step - loss: 0.7118 - accuracy: 0.5576 - val_loss: 0.6740 - val_accuracy: 0.5714 Epoch 3/20 1024/1024 [==============================] - 0s 318us/step - loss: 0.6760 - accuracy: 0.5967 - val_loss: 0.6752 - val_accuracy: 0.5491 Epoch 4/20 1024/1024 [==============================] - 0s 315us/step - loss: 0.6575 - accuracy: 0.6123 - val_loss: 0.7017 - val_accuracy: 0.5446 Epoch 5/20 1024/1024 [==============================] - 0s 320us/step - loss: 0.6534 - accuracy: 0.6357 - val_loss: 0.6257 - val_accuracy: 0.6562 Epoch 6/20 1024/1024 [==============================] - 0s 321us/step - loss: 0.6425 - accuracy: 0.6338 - val_loss: 0.6193 - val_accuracy: 0.6607 Epoch 7/20 1024/1024 [==============================] - 0s 326us/step - loss: 0.6545 - accuracy: 0.6270 - val_loss: 0.6803 - val_accuracy: 0.5670 Epoch 8/20 1024/1024 [==============================] - 0s 384us/step - loss: 0.6415 - accuracy: 0.6279 - val_loss: 0.6223 - val_accuracy: 0.6562 Epoch 9/20 1024/1024 [==============================] - 0s 328us/step - loss: 0.6300 - accuracy: 0.6621 - val_loss: 0.6519 - val_accuracy: 0.6250 Epoch 10/20 1024/1024 [==============================] - 0s 342us/step - loss: 0.6602 - accuracy: 0.6270 - val_loss: 0.6541 - val_accuracy: 0.5982 Epoch 11/20 1024/1024 [==============================] - 0s 319us/step - loss: 0.6431 - accuracy: 0.6357 - val_loss: 0.6762 - val_accuracy: 0.5804 Epoch 12/20 1024/1024 [==============================] - 0s 315us/step - loss: 0.6469 - accuracy: 0.6416 - val_loss: 0.6106 - val_accuracy: 0.6562 Epoch 13/20 1024/1024 [==============================] - 0s 317us/step - loss: 0.6266 - accuracy: 0.6504 - val_loss: 0.6183 - val_accuracy: 0.6473 Epoch 14/20 1024/1024 [==============================] - 0s 315us/step - loss: 0.6739 - accuracy: 0.6074 - val_loss: 0.6026 - val_accuracy: 0.6696 Epoch 15/20 1024/1024 [==============================] - 0s 340us/step - loss: 0.6656 - accuracy: 0.6230 - val_loss: 0.6041 - val_accuracy: 0.6786 Epoch 16/20 1024/1024 [==============================] - 0s 348us/step - loss: 0.6573 - accuracy: 0.6250 - val_loss: 0.6332 - val_accuracy: 0.6473 Epoch 17/20 1024/1024 [==============================] - 0s 319us/step - loss: 0.6282 - accuracy: 0.6367 - val_loss: 0.6004 - val_accuracy: 0.6875 Epoch 18/20 1024/1024 [==============================] - 0s 325us/step - loss: 0.6255 - accuracy: 0.6504 - val_loss: 0.6119 - val_accuracy: 0.6652 Epoch 19/20 1024/1024 [==============================] - 0s 324us/step - loss: 0.6500 - accuracy: 0.6387 - val_loss: 0.6122 - val_accuracy: 0.6741 Epoch 20/20 1024/1024 [==============================] - 0s 333us/step - loss: 0.6381 - accuracy: 0.6426 - val_loss: 0.6193 - val_accuracy: 0.6518 Train on 1024 samples, validate on 224 samples Epoch 1/20 1024/1024 [==============================] - 2s 2ms/step - loss: 7.0027 - accuracy: 0.5645 - val_loss: 0.5039 - val_accuracy: 0.7946 Epoch 2/20 1024/1024 [==============================] - 2s 2ms/step - loss: 1.0776 - accuracy: 0.6787 - val_loss: 1.1147 - val_accuracy: 0.5580 Epoch 3/20 1024/1024 [==============================] - 2s 2ms/step - loss: 0.8585 - accuracy: 0.6904 - val_loss: 0.4512 - val_accuracy: 0.8036 Epoch 4/20 1024/1024 [==============================] - 2s 2ms/step - loss: 0.8361 - accuracy: 0.7334 - val_loss: 0.4642 - val_accuracy: 0.7500 Epoch 5/20 1024/1024 [==============================] - 2s 2ms/step - loss: 0.6008 - accuracy: 0.7627 - val_loss: 0.5468 - val_accuracy: 0.7589 Epoch 6/20 1024/1024 [==============================] - 2s 2ms/step - loss: 0.5693 - accuracy: 0.7930 - val_loss: 0.3864 - val_accuracy: 0.8259 Epoch 7/20 1024/1024 [==============================] - 2s 2ms/step - loss: 0.3972 - accuracy: 0.8408 - val_loss: 0.4960 - val_accuracy: 0.7812 Epoch 8/20 1024/1024 [==============================] - 2s 2ms/step - loss: 0.3658 - accuracy: 0.8564 - val_loss: 0.4218 - val_accuracy: 0.8036 Epoch 9/20 1024/1024 [==============================] - 2s 2ms/step - loss: 0.2635 - accuracy: 0.8936 - val_loss: 1.2127 - val_accuracy: 0.6205 Epoch 10/20 1024/1024 [==============================] - 2s 2ms/step - loss: 0.2591 - accuracy: 0.8896 - val_loss: 0.5767 - val_accuracy: 0.7679 Epoch 11/20 1024/1024 [==============================] - 2s 2ms/step - loss: 0.2713 - accuracy: 0.8965 - val_loss: 0.4964 - val_accuracy: 0.8036 Epoch 12/20 1024/1024 [==============================] - 2s 2ms/step - loss: 0.2399 - accuracy: 0.9180 - val_loss: 0.5987 - val_accuracy: 0.7679 Epoch 13/20 1024/1024 [==============================] - 2s 2ms/step - loss: 0.1860 - accuracy: 0.9336 - val_loss: 0.4666 - val_accuracy: 0.8259 Epoch 14/20 1024/1024 [==============================] - 2s 2ms/step - loss: 0.1838 - accuracy: 0.9355 - val_loss: 0.6134 - val_accuracy: 0.7857 Epoch 15/20 1024/1024 [==============================] - 2s 2ms/step - loss: 0.1536 - accuracy: 0.9502 - val_loss: 0.6403 - val_accuracy: 0.7857 Epoch 16/20 1024/1024 [==============================] - 2s 2ms/step - loss: 0.1643 - accuracy: 0.9463 - val_loss: 0.5881 - val_accuracy: 0.7902 Epoch 17/20 1024/1024 [==============================] - 2s 2ms/step - loss: 0.1121 - accuracy: 0.9697 - val_loss: 0.6115 - val_accuracy: 0.8259 Epoch 18/20 1024/1024 [==============================] - 2s 2ms/step - loss: 0.2150 - accuracy: 0.9492 - val_loss: 0.7794 - val_accuracy: 0.7768 Epoch 19/20 1024/1024 [==============================] - 2s 2ms/step - loss: 0.0903 - accuracy: 0.9668 - val_loss: 0.8682 - val_accuracy: 0.7723 Epoch 20/20 1024/1024 [==============================] - 2s 2ms/step - loss: 0.1591 - accuracy: 0.9668 - val_loss: 0.7164 - val_accuracy: 0.8125
In [42]:
test_labels = np.array([0 if "slow_food" in f else 1 for f in test_batches.filenames])[
: len(bottleneck_features_test)
]
y_test_pred = model.predict_classes(bottleneck_features_test)
accuracy = np.count_nonzero(
y_test_pred[: len(test_labels)].ravel() == test_labels
) / len(test_labels)
print("\nThe accuracy is: " + str(accuracy))
The accuracy is: 0.8035714285714286
In [43]:
plot_accuracy_loss(history_2)
This model is the most promising one.
In [44]:
cm = confusion_matrix(test_labels, np.round(y_test_pred[: len(test_labels)]))
In [45]:
plot_confusion_matrix(cm, classes, title="Confusion Matrix")
Confusion matrix, without normalization [[82 30] [14 98]]
In [46]:
plot_confusion_matrix(cm, classes, title="Confusion Matrix", normalize=True)
Normalized confusion matrix [[0.73214286 0.26785714] [0.125 0.875 ]]
Prediction on the test set are also much more accurate thane previous ones.
In [47]:
c = list(zip([i.split("\\")[1] for i in test_batches.filenames], y_test_pred))
random.shuffle(c)
filenames_3, preds_3 = zip(*c)
In [48]:
wrong_predictions_3 = plot_predictions_or_examples(filenames_3, preds_3)
In [49]:
wrong_predictions_3
Out[49]:
['slow_food 9.salmon.jpg', 'slow_food 42.47425871_401.jpg', 'fast_food 66.n_9726-1.jpg', 'slow_food 34.healthy-fruits-1296x728.jpg', 'slow_food 72.Classic-Chicken-Salad-2.jpg', 'slow_food 76.Moroccan-Chicken-with-Green-Beans.jpg', 'slow_food 71.74b153_fit-meal-catering-dietetyczny-lodz-lodzkie-zdjecia.jpg', 'slow_food 79.fresh_fruit_salad_61942_16x9.jpg', 'fast_food 13.pizza-930x530.jpg', 'fast_food 54.f42dbda63b75be1fc3251648954e1426.jpg', 'slow_food 63.secret-to-a-healthy-heart.jpg', 'slow_food 38.iStock-588354332-5a4baf7c7bb28300379b7522.jpg', 'slow_food 82.easy-kielbasa-sheet-pan-dinner-with-veggies-image.jpg']
Tensorboard¶
In [53]:
from tensorboard import notebook
notebook.list() # View open TensorBoard instances
Known TensorBoard instances: - port 6006: logdir logs/fit (started 10:40:12 ago; pid 21392)
In [54]:
notebook.display(port=6006, height=1000)
Selecting TensorBoard with logdir logs/fit (started 10:40:23 ago; port 6006, pid 21392).